Estimation device and estimation method

ABSTRACT

A measurement device is configured to set an observation surface on a surface of a structure as a measurement surface to measure a change of the measurement surface as a measurement surface change vector. An estimator is configured to generate an estimation model based on a shape model obtained by modeling a shape of the structure. The estimator is configured to acquire a coefficient vector by solving a norm minimization problem by setting, as parameters, a measurement surface change vector and a part of the estimation model. The coefficient vector forms a sparse solution. The estimator is configured to estimate a change of a crack occurrence surface by determining a candidate surface, which is inside the structure and assumed to have a crack, as the crack occurrence surface, based on the coefficient vector and another part of the estimation model.

BACKGROUND OF THE INVENTION 1. Field of the Invention

This disclosure relates to an estimation device and an estimationmethod.

2. Description of the Related Art

In general, a crack caused inside a rotator structure of a rotaryelectric machine to be applied to a turbine generator cannot beinspected with eyes, and thus there is a fear in that the crack is notdetected in a usual inspection to result in expansion thereof, and thelifetime of the structure, for example, the turbine generator having therotator structure, is affected disadvantageously.

In view of this, hitherto, there has been known a crack size estimationmethod being a non-destructive inspection method involving measuringstrain of a surface of a structure to inspect a crack caused inside thestructure (e.g., refer to Japanese Patent Application Laid-open No.2012-159477).

The above-mentioned crack size estimation method easily achievesdownsizing of an apparatus compared to other non-destructive inspectionmethods, such as an ultrasonic inspection method or an X-ray inspectionmethod, which enables cost reduction.

However, the above-mentioned crack size estimation method is not amethod of directly measuring a crack caused inside the structure.

The above-mentioned crack size estimation method uses inverse analysisof deriving a crack caused inside the structure based on a change inshape of the surface of the structure, to thereby estimate a positionand size of the crack caused inside the structure.

An inverse problem is required to be solved to perform inverse analysis.In order to solve the inverse problem, the following three conditionsare required to be satisfied. Specifically, the solution of the inverseproblem is required to be uniquely determined as uniqueness of thesolution, the solution of the inverse problem is required to exist asexistence of the solution, and stability of the inverse problem isrequired to be achieved as stability of the solution.

However, those three conditions, namely, the uniqueness of the solution,the existence of the solution, and the stability of the solution may notbe satisfied depending on the results of measurements of strain.

When at least one condition is not satisfied among the above-mentionedthree conditions, the inverse problem becomes an ill-posed problem,namely, an inappropriate problem. As a result, the accuracy ofestimating the crack deteriorates.

SUMMARY OF THE INVENTION

This disclosure has been made to solve the above-mentioned problem, andhas an object to acquire an estimation device and an estimation method,which are capable of accurately estimating a crack caused inside astructure.

According to at least one embodiment of this disclosure, there isprovided an estimation device including: a measurement device configuredto set an observation surface on a surface of a structure as ameasurement surface to measure a change of the measurement surface as ameasurement surface change vector; and an estimator configured toestimate a change of a crack occurrence surface by determining acandidate surface, which is inside the structure and assumed to have acrack, as the crack occurrence surface, based on: a coefficient vectorforming a sparse solution acquired by solving a norm minimizationproblem by setting, as parameters, the measurement surface change vectorand a part of an estimation model, which is generated from a shape modelobtained by modeling a shape of the structure; and another part of theestimation model.

According to at least one embodiment of this disclosure, the crackcaused inside the structure can be accurately estimated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram for illustrating an example of a configurationof an estimation device according to a first embodiment of thisdisclosure.

FIG. 2 is a diagram for illustrating an example of a state in which atensile load is applied to a plate being a structure to be subjected toestimation by the estimation device of FIG. 1.

FIG. 3 is a diagram for illustrating an example of a state in which abending moment is applied to the plate of FIG. 2.

FIG. 4 is a diagram for illustrating an example of reference coordinatesset for candidate surfaces of FIG. 2 and FIG. 3.

FIG. 5 is a diagram for illustrating an example of each section at atime of division of the candidate surface of FIG. 4.

FIG. 6 is a diagram for illustrating an example of reference coordinatesset for observation surfaces of FIG. 2 and FIG. 3.

FIG. 7 is a diagram for illustrating an example of each section at atime of division of the observation surface of FIG. 6.

FIG. 8 is a diagram for illustrating a displacement change vectorindicating a difference being a displacement change of each node foreach position of a crack on the candidate surface of FIG. 5.

FIG. 9 is a diagram for illustrating a crack surface matrix includingthe plurality of displacement change vectors of FIG. 8.

FIG. 10 is a diagram for illustrating a strain change vector indicatinga difference in strain of each node on the observation surface of FIG. 7for each position of the crack on the candidate surface of FIG. 5.

FIG. 11 is a diagram for illustrating a measurement surface matrixincluding the plurality of strain change vectors of FIG. 10.

FIG. 12 is a diagram for illustrating each expression to be processed bya crack state estimator of FIG. 1.

FIG. 13 is a flow chart for illustrating processing to be executed bythe estimation device of FIG. 1.

FIG. 14 is a flow chart for illustrating learning data creationprocessing included in the flow chart of FIG. 13.

FIG. 15 is a flow chart for illustrating processing subsequent to theflow chart of FIG. 14.

FIG. 16 is a flow chart for illustrating estimation processing includedin the flow chart of FIG. 13.

FIG. 17 is a schematic view of a coordinate system at a time when aninternal pressure is applied to an inside of a column member serving asthe structure to be subjected to estimation by the estimation device ofFIG. 1.

FIG. 18 is a plan view of the column member of FIG. 17.

FIG. 19 is a flow chart for illustrating learning data creationprocessing in a second embodiment of this disclosure.

FIG. 20 is a flow chart for illustrating processing subsequent to theflow chart of FIG. 19.

FIG. 21 is a diagram for illustrating an example of a side of a crackoccurrence surface determined at a time of boundary condition assignmentprocessing of FIG. 19.

FIG. 22 is a diagram for illustrating a first example of a nodeidentified as a crack on a crack occurrence surface determined at thetime of the boundary condition assignment processing of FIG. 19.

FIG. 23 is a diagram for illustrating a second example of the nodeidentified as a crack on the crack occurrence surface determined at thetime of the boundary condition assignment processing of FIG. 19.

FIG. 24 is a diagram for illustrating a third example of the nodeidentified as a crack on the crack occurrence surface determined at thetime of the boundary condition assignment processing of FIG. 19.

FIG. 25 is a flow chart for illustrating the boundary conditionassignment processing of FIG. 19.

FIG. 26 is a diagram for illustrating a displacement change vectorindicating a difference being a displacement change of each node on theobservation surface for each position of a crack on the candidatesurface in a third embodiment of this disclosure.

FIG. 27 is a diagram for illustrating a measurement surface matrixincluding the plurality of displacement change vectors of FIG. 26.

FIG. 28 is a diagram for illustrating an angle change vector indicatingan angle of each node on the observation surface for each position of acrack on the candidate surface in the third embodiment.

FIG. 29 is a diagram for illustrating a measurement surface matrixincluding the plurality of angle change vectors of FIG. 28.

FIG. 30 is a diagram for illustrating a load change vector indicating aload on each node for each position of a crack on the candidate surfacein a fourth embodiment of this disclosure.

FIG. 31 is a diagram for illustrating a crack surface matrix includingthe plurality of load change vectors of FIG. 30.

FIG. 32 is a block diagram for illustrating an example of aconfiguration of an estimation device according to a fifth embodiment ofthis disclosure.

FIG. 33 is a flow chart for illustrating processing to be executed bythe estimation device of FIG. 32.

FIG. 34 is a flow chart for illustrating output processing of FIG. 13and FIG. 33 in a sixth embodiment of this disclosure.

FIG. 35 is a flow chart for illustrating output processing of FIG. 13and FIG. 33 in a seventh embodiment of this disclosure.

FIG. 36 is a diagram for illustrating a candidate surface in an eighthembodiment of this disclosure.

FIG. 37 is a flow chart for illustrating processing of determining thecandidate surface of FIG. 36.

FIG. 38 is a diagram for illustrating an example of a hardwareconfiguration.

FIG. 39 is a diagram for illustrating another example of the hardwareconfiguration.

DESCRIPTION OF THE EMBODIMENTS First Embodiment

FIG. 1 is a block diagram for illustrating an example of a configurationof an estimation device according to a first embodiment of thisdisclosure. FIG. 2 is a diagram for illustrating an example of a statein which a tensile load F1 is applied to a plate 5 being a structure tobe subjected to estimation by the estimation device of FIG. 1. FIG. 3 isa diagram for illustrating an example of a state in which a bendingmoment F2 is applied to the plate 5 of FIG. 2.

As illustrated in FIG. 1, the estimation device includes a measurementdevice 3 and an estimator 1. The position and the size of a crack 55 onthe plate 5 of FIG. 2 are estimated by the estimator 1.

A candidate surface 53 can be set inside the plate 5. Further, anobservation surface 51 can be set on the surface of the plate 5. In FIG.2 and FIG. 3, the plate 5 is represented in an orthogonal coordinatesystem. The candidate surface 53 is set to a position at which the crack55 is assumed to occur. The observation surface 51 is set within such arange that the surface of the plate 5 changes due to a change of thecandidate surface 53.

The measurement device 3 is configured to set at least a part of thesurface of the plate 5 as the observation surface 51 to measure a changeof the surface of the observation surface 51. The measurement device 3includes a strain gauge, for example. The strain gauge is mounted to theobservation surface 51 to be used.

The strain gauge is formed of a base material and a resistance material.The base material is formed of an electrical insulator. The resistancematerial is mounted on the base material. In the base material, alead-out line is provided at a portion protruding from the basematerial. The base material is mounted on the surface of the plate 5with an adhesive. With such a mounting structure, when the base materialexpands or contracts, the resistance material also expands or contracts,and the electrical resistance of the resistance material changes. Thelead-out line is connected to the estimator 1.

For example, when strain occurs on the surface of the plate 5, theresistance material expands or contracts, and thus the electricalresistance of the resistance material changes. The change of theelectrical resistance is transferred to the estimator 1 via the lead-outline. As a result, the strain gauge measures a change in strain of thesurface of the plate 5, and supplies the measurement result to theestimator 1.

In short, the measurement device 3 measures the change in strain of theobservation surface 51, which is on the surface of the plate 5, with thetensile load F1 of FIG. 2 or the bending moment F2 of FIG. 3 beingapplied to the plate 5.

The measurement device 3 measures, as a measurement surface changevector, a change of the measurement surface by setting the observationsurface 51 as the measurement surface, and details of this processingare described later.

The estimator 1 is configured to estimate the crack 55 inside the plate5 based on the change of the measurement surface measured by themeasurement device 3. That is, the estimator 1 estimates the crack 55inside the plate 5 by performing inverse analysis that uses arelationship between the change in shape of the surface of the plate 5and the crack 55 inside the plate 5.

Specifically, various kinds of phases to be processed by the estimator 1include a learning phase and an inverse analysis phase serving as autilization phase. The inverse analysis phase is processed by theestimator 1 after the learning phase.

In the learning phase, a relationship between the crack 55 inside theplate 5 and a change in shape of the surface of the plate 5 is preparedas learning data in advance. Further, in the inverse analysis phase,learning data prepared in the learning phase is used to estimate theposition and size of the crack 55 inside the plate 5.

The learning data and a least squares method are usually used for suchestimation, and thus a pseudo-inverse matrix is required to be acquired.With this, such estimation is reduced to solving an inverse problem. Inorder to solve the inverse problem, three conditions, namely, uniquenessof the solution, existence of the solution, and stability of thesolution are required to be satisfied.

However, the above-mentioned three conditions may not be satisfieddepending on the learning data and the results of measuring strain ofthe measurement surface by the measurement device 3.

For example, when the number of unknown variables is larger than that ofobserved values, there are an infinite number of solutions, and thusuniqueness of the solution is not satisfied.

Further, for example, when the number of unknown variables is smallerthan that of observed values, there is no solution, and thus existenceof the solution is not satisfied.

Further, for example, even in a case where strain occurs on the plate 5due to stress on the plate 5, when influence of the strain rapidlyattenuates as the influence becomes away from a location at which thestrain has occurred, stability of the solution is not satisfied.

Thus, the inverse problem may become an ill-posed problem, namely, aninappropriate problem.

Therefore, even when the position and size of the crack 55 are estimatedby using the learning data, there may be no pseudo-inverse matrix insuch an inappropriate problem.

In view of this, in the first embodiment, the estimator 1 models theshape of the plate 5 to acquire a shape model. The estimator 1 generatesan estimation model based on the shape model. The estimator 1 acquires acoefficient vector forming a sparse solution by solving a normoptimization problem by setting, as parameters, a part of the estimationmodel and the measurement surface change vector serving. The estimator 1estimates the change of the crack occurrence surface by setting thecandidate surface 53 as a crack occurrence surface based on thecoefficient vector and another part of the estimation model.

Specifically, the estimator 1 includes a model generator 11, a crackstate analyzer 12, a storage 13, and an analysis result output module14.

The model generator 11 includes a shape model generator 111 and anestimation model generator 112.

The shape model generator 111 is configured to generate a shape model.The estimation model generator 112 is configured to generate a structureanalysis model based on the shape model. The estimation model generator112 is configured to generate an estimation model based on the structureanalysis model.

The generated estimation model is different depending on the structureanalysis model. The structure analysis model is a model used at the timeof performing structure analysis. The structure analysis model and aboundary condition for the structure analysis model are required inorder to perform structure analysis.

The boundary condition includes a load condition and a constraintcondition. Three conditions, namely, the structure analysis model, theload condition, and the constraint condition are required in order toperform structure analysis.

Thus, a load condition and a constraint condition are defined whenstructure analysis is performed by using the structure analysis model.

The load condition defines information on a location of the structure towhich a load is applied, and a magnitude of the load, namely,information on a force vector at the location to which the load isapplied in the structure model.

Meanwhile, the constraint condition defines information on a location ofthe structure to be supported and how the location is to be supported,namely, information that sets the amount of deformation at the locationsupported in the structure analysis model to zero.

The boundary condition is a condition that is different depending on thegenerated shape model.

The shape model is a model of an object to be inspected, which isgenerated as the entire plate 5 or a part of the plate 5 based on themeasurement surface and the crack occurrence surface.

When the entire plate 5 is set as a shape model, a temperaturedistribution may be added as the boundary condition.

The temperature distribution is used in the following manner. First,known information on a uniform temperature distribution is applied tothe structure analysis model as a load under a set initial temperature.Next, structure analysis is performed by causing, under an analysistemperature different from the set initial temperature, the entire plate5 to expand or contract due to a difference between the initialtemperature and the analysis temperature.

Meanwhile, when a part of the plate 5 is set as the shape model,information on displacement change of a surface cut out as the part ofthe plate 5 and information on a load distribution are given as theboundary condition.

In this manner, when structure analysis is performed based on theboundary condition, a model in which the measurement surface and crackoccurrence surface of the shape model are divided into lattices is usedas the structure analysis model.

Thus, the crack occurrence surface is generated as a part of thestructure analysis model by dividing the candidate surface 53 intolattices. Further, the measurement surface is generated as another partof the structure analysis model by dividing the observation surface 51into lattices.

FIG. 4 is a diagram for illustrating an example of reference coordinatesset for the candidate surfaces 53 of FIG. 2 and FIG. 3. FIG. 5 is adiagram for illustrating an example of each section A at a time ofdivision of the candidate surface 53 of FIG. 4.

As illustrated in FIG. 5, the crack occurrence surface of the structureanalysis model is divided into a plurality of sections A. Each section Ais acquired by dividing the crack occurrence surface into n sectionsalong an X-axis direction and dividing each of those n sections into msections along a Y-axis direction.

In other words, the crack occurrence surface in the shape model isdivided into n(horizontal)×m(vertical) lattices, and as a result, thoselattices intersect with each other and the plurality of sections A arecreated on the crack occurrence surface of the structure analysis model.

Coordinates of each lattice are represented by (i, j). The origin of (i,j) is (0, 0). A position of the maximum coordinates of (i, j) is (n, m).When each lattice is set as a node, each node is a point on a lineforming the section A.

Structure analysis of the crack occurrence surface is performed for eachposition of each node on the crack occurrence surface.

For example, when the crack 55 occurs at a node at the position of (0,0) on the crack occurrence surface, structure analysis is performed fordisplacement changes of all the nodes on the crack occurrence surface,which exist at the positions of from (0, 0) to (n, m) on the crackoccurrence surface. In this case, a node at the position of (0, 0)corresponds to the crack 55, and thus is vacant. Thus, there is nodisplacement change of the position of (0, 0). Meanwhile, it is assumedthat there is no crack 55 at a node at a position other than theposition of (0, 0), and thus a displacement change occurs depending onthe boundary condition.

Next, when the crack 55 occurs at a node at the position of (0, 1) onthe crack occurrence surface, structure analysis is performed fordisplacement changes of all the nodes on the crack occurrence surface,which exist at the positions of from (0, 0) to (n, m) on the crackoccurrence surface. In this case, a node at the position of (0, 1)corresponds to the crack 55, and thus is vacant. Thus, there is nodisplacement change of the position of (0, 1). Meanwhile, it is assumedthat there is no crack 55 at a node at a position other than theposition of (0, 1), and thus a displacement change occurs depending onthe boundary condition.

After that, also for nodes at positions other than the positions of (0,0) and (0, 1) on the crack occurrence surface, structure analysis issimilarly performed for displacement changes of all the nodes on thecrack occurrence surface. That is, each position of each node on thecrack occurrence surface is assumed to have the crack 55, anddisplacement changes are acquired for positions of all the nodes on thecrack occurrence surface. Among the displacement changes acquired inthis manner, at least information on the maximum displacement change isstored into the storage 13. The order of positions of nodes set as thecrack 55 in the above description are determined in advance.

In other words, the following relationship between the boundarycondition and each node on the crack occurrence surface is set. Changeamounts in all the directions are set to zero for nodes on the crackoccurrence surface to which the constraint condition is set. With this,a node on the crack occurrence surface to which the constraint conditionis set does not move. Meanwhile, a change amount in a fixed direction isset to a value other than zero for a node that does not have the crack55 among nodes on the crack occurrence surface to which the loadcondition is set. Further, change amounts in all the directions are setto zero for a node that has the crack 55 among nodes on the crackoccurrence surface to which the load condition is set.

FIG. 6 is a diagram for illustrating an example of reference coordinatesset for the observation surface 51 of FIG. 2 and FIG. 3. FIG. 7 is adiagram for illustrating an example of each section B at a time ofdivision of the observation surface 51 of FIG. 6.

As illustrated in FIG. 7, the measurement surface of the structureanalysis model is divided into a plurality of sections B. Each section Bis acquired by dividing the crack occurrence surface into n sectionsalong an X-axis direction and dividing each of those n sections into psections along a Z-axis direction.

In other words, the measurement surface in the shape model is dividedinto n(horizontal)×p(vertical) lattices, and as a result, those latticesintersect with each other and the plurality of sections B are created onthe crack occurrence surface of the structure analysis model.

Coordinates of each lattice are represented by (k, l). The origin of (k,l) is (0, 0). A position of the maximum coordinates of (k, l) is (n, p).When each lattice is set as a node, each node is a point on a lineforming the section B.

Structure analysis of the measurement surface is performed for eachposition of each node on the crack occurrence surface.

For example, when the crack 55 occurs at a node at the position of (0,0) on the crack occurrence surface, structure analysis is performed forstrain changes of all the nodes on the measurement surface, which existat the positions of from (0, 0) to (n, p) on the measurement surface.

Next, when the crack 55 occurs at a node at the position of (0, 1) onthe crack occurrence surface, structure analysis is performed for strainchanges of all the nodes on the measurement surface, which exist at thepositions of from (0, 0) to (n, p) on the measurement surface.

After that, also for nodes at positions other than the positions of (0,0) and (0, 1) on the crack occurrence surface, structure analysis issimilarly performed for strain changes of all the nodes on themeasurement surface, which exist at the positions of from (0, 0) to (n,p) on the measurement surface. That is, each position of each node onthe crack occurrence surface is assumed to have the crack 55, and strainchanges are acquired for positions of all the nodes on the measurementsurface. Among the strain changes acquired in this manner, at leastinformation on the maximum strain change is stored into the storage 13.

In other words, the following relationship between the boundarycondition and each node on the measurement surface is set. Changeamounts in all the directions are set to zero for nodes on themeasurement surface to which the constraint condition is set. With this,a node on the measurement surface to which the constraint condition isset does not move. Meanwhile, a change amount in a fixed direction isset to a value other than zero for a node on the measurement surface towhich the load condition is set.

Further, principal strain, equivalent strain defined by a Tresca yieldcriterion, or equivalent strain defined by a Mises yield criterion maybe used as the strain in the case of applying the tensile load F1 on theZ-axis or bending moment F2 on the ZX-axis.

In summary of the above description, the model generator is configuredto perform structure analysis based on the boundary condition set inadvance for the shape model generated based on the measurement surfaceand the crack occurrence surface. The model generator 11 is configuredto perform structure analysis to generate a plurality of measurementsurface estimated change vectors each estimating a change of themeasurement surface. The model generator 11 is configured to performstructure analysis to generate, as a change of the crack occurrencesurface, a plurality of crack occurrence surface estimated changevectors each estimating a displacement change of the crack occurrencesurface. The model generator 11 is configured to generate an estimationmodel including a measurement surface estimated change vector and acrack occurrence surface estimated change vector.

Specifically, the model generator 11 assigns a boundary conditionspecifying that the crack 55 does not occur to each node of the crackoccurrence surface in the structure analysis model. The model generator11 calculates an amount of displacement change of each node on the crackoccurrence surface in the structure analysis model. The model generator11 calculates strain of each node as deformation of the node on themeasurement surface in the structure analysis model.

Further, the model generator 11 assigns a boundary condition specifyingthat each node of the crack occurrence surface has the crack 55 to eachnode on the crack occurrence surface in the structure analysis model.Similarly to the above description, the model generator 11 calculatesthe amount of displacement change of each node on the crack occurrencesurface and strain of each node being deformation of the node on themeasurement surface.

The model generator 11 creates a displacement change vector indicating adifference being an amount of displacement change of a node on the crackoccurrence surface in the structure analysis model.

FIG. 8 is a diagram for illustrating a displacement change vectorindicating a difference being an amount of displacement change of eachnode for each position of the crack 55 on the candidate surface 53 ofFIG. 5. As illustrated in FIG. 8, pieces of displacement data onrespective nodes included in a column vector of Δ(-, -) are arranged inan order of moving the crack 55 assumed for each of those nodes. In thiscase, “-” indicates indefinite data without meaning. Also in thefollowing description, “-” similarly indicates indefinite data withoutmeaning.

For example, δ(i, j) represents a displacement change of a node at aposition (i, j) on the candidate surface 53 of FIG. 5.

Further, the position of the crack 55 at the time of structure analysisis assigned to this column vector, and the position of the crack 55 atthe time of structure analysis is assigned to displacement data on eachnode included in this column vector.

FIG. 9 is a diagram for illustrating a crack surface matrixΔ_(crack_diff) including the plurality of displacement change vectors ofFIG. 8. Each of the plurality of displacement change vectors of FIG. 8includes a column vector. The crack surface matrix Δ_(crack_diff) ofFIG. 9 is acquired by arranging those column vectors in the order ofmoving the crack 55 assumed for each node. The crack surface matrixΔ_(crack_diff) is represented by Expression (1) given below.

$\begin{matrix}{\Delta_{{crack}\_{diff}} = \begin{pmatrix}{\delta_{0,0}\left( {0,} \right)} & \ldots & {\delta_{i,j}\left( {0,0} \right)} & \ldots & {\delta_{n,m}\left( {0,0} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{\delta_{0,0}\left( {i,l} \right)} & \ldots & {\delta_{i,j}\left( {i,j} \right)} & \ldots & {\delta_{n,m}\left( {i,j} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{\delta_{0,0}\left( {n,m} \right)} & \ldots & {\delta_{i,j}\left( {n,m} \right)} & \ldots & {\delta_{n,m}\left( {n,m} \right)}\end{pmatrix}} & (1)\end{matrix}$

Further, the model generator 11 creates a strain change vectorindicating a difference in strain of a node on the measurement surfacein the structure analysis model.

FIG. 10 is a diagram for illustrating a strain change vector indicatinga difference in strain of each node on the observation surface 51 ofFIG. 7 for each position of the crack 55 on the candidate surface 53 ofFIG. 5. As illustrated in FIG. 10, pieces of strain data on respectivenodes included in a column vector of E(-, -) are arranged in an order ofmoving the crack 55 assumed for each of those nodes.

For example, ε(i, j) represents a strain change of a node at a position(i, j) on the observation surface 51 of FIG. 7.

Further, the position of the crack 55 at the time of structure analysisis assigned to this column vector, and the position of the crack 55 atthe time of structure analysis is assigned to strain data on each nodeincluded in this column vector.

FIG. 11 is a diagram for illustrating a measurement surface matrixE_(measure) including the plurality of strain change vectors of FIG. 10.Each of the plurality of strain change vectors of FIG. 10 include acolumn vector. The measurement surface matrix E_(measure) of FIG. 11 isacquired by arranging those column vectors in the order of moving thecrack 55 assumed for each node. The measurement surface matrixE_(measure) is represented by Expression (2) given below.

$\begin{matrix}{E_{measure} = \begin{pmatrix}{ɛ_{0,0}\left( {0,} \right)} & \ldots & {ɛ_{i,j}\left( {0,0} \right)} & \ldots & {ɛ_{n,m}\left( {0,0} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{ɛ_{0,0}\left( {k,l} \right)} & \ldots & {ɛ_{i,j}\left( {k,l} \right)} & \ldots & {ɛ_{n,m}\left( {k,l} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{ɛ_{0,0}\left( {n,p} \right)} & \ldots & {ɛ_{i,j}\left( {n,p} \right)} & \ldots & {ɛ_{n,m}\left( {n,p} \right)}\end{pmatrix}} & (1)\end{matrix}$

A change in strain of the observation surface 51 on the plate 5 ismeasured by the measurement device 3 for each of a condition specifyingthat no crack 55 has occurred inside the plate 5 and a conditionspecifying that the crack 55 has occurred inside the plate 5. Adifference between strain of the observation surface 51 under thecondition specifying that no crack 55 has occurred inside the plate 5and strain of the observation surface 51 under the condition specifyingthat the crack 55 has occurred inside the plate 5 is acquired.Expression (3) given below represents arrangement of acquired straindifferences as column vectors in the same order as that of moving thecrack 55 assumed for each node.

$\begin{matrix}{\overset{\sim}{ɛ} = \begin{bmatrix} \\\vdots \\

\end{bmatrix}} & (3)\end{matrix}$

The measurement device 3 measures the column vectors represented byExpression (3) as the measurement surface change vector. A suffix of 0*0in the measurement surface change vector represents a node at theposition (0, 0) on the observation surface 51 of FIG. 7. Further, forexample, a suffix of n p in the measurement surface change vectorrepresents a node at the position (n, p) on the observation surface 51of FIG. 7.

The crack state analyzer 12 includes a data acquisition module 121 and acrack state estimator 122.

The data acquisition module 121 is configured to acquire the measurementsurface change vector measured by the measurement device 3. The dataacquisition module 121 is configured to supply the measurement surfacechange vector acquired from the measurement device 3 to the crack stateestimator 122.

FIG. 12 is a diagram for illustrating each expression to be processed bythe crack state estimator 122 of FIG. 1.

The crack state estimator 122 includes a similar vector extractor 1221,a feature extractor 1222, and a crack analyzer 1223.

The similar vector extractor 1221 is configured to set each of thecolumn vectors forming the measurement surface matrix E_(measure)represented by Expression (2) as the measurement surface estimatedchange vector. The measurement surface estimated change vector isrepresented by Expression (4) given below.

$\begin{matrix}{{dɛ}_{{crack}_{i,j}} = \begin{bmatrix}{ɛ_{i,j}\left( {0,0} \right)} \\\vdots \\{ɛ_{i,j}\left( {n,p} \right)}\end{bmatrix}} & (4)\end{matrix}$

The similar vector extractor 1221 sets, as a measurement surface similarchange vector, a measurement surface estimated change vector whosesimilarity with the measurement surface change vector represented byExpression (3) is higher than a reference similarity set in advance. Thesimilar vector extractor 1221 uses a cosine similarity as thesimilarity, and sets the reference similarity to 0, for example.

The cosine similarity is represented by Expression (5) given below.

$\begin{matrix}{\frac{{dɛ}_{{crack}_{i,j}}^{T} \cdot \overset{\sim}{ɛ}}{{{dɛ}_{{crack}_{i,j}}^{T}} \cdot {\overset{\sim}{ɛ}}} = \gamma} & (5)\end{matrix}$

The measurement surface similar change vector is represented byExpression (6) given below.E′ _(measure)=[ . . . , dε _(crack) _(i,j) ]  (6)

The similar vector extractor 1221 sets each of the column vectorsforming the crack surface matrix Δ_(crack_diff) represented byExpression (1) as the crack occurrence surface estimated change vector.The similar vector extractor 1221 sets a crack occurrence surfaceestimated change vector corresponding to the measurement surface similarchange vector represented by Expression (6) as a crack occurrencesurface similar change vector.

The crack occurrence surface similar change vector is represented byExpression (7) given below.Δ′_(crack_diff)=[ . . . , dδ _(crack_diff) _(i,j) ]  (7)

The similar vector extractor 1221 extracts the measurement surfacesimilar change vector represented by Expression (6) serving as a part ofthe estimation model and the crack occurrence surface similar changevector represented by Expression (7) serving as another part of theestimation model.

The feature extractor 1222 is configured to solve a L1-norm minimizationproblem, which is a norm minimization problem, to extract a coefficientvector based on the measurement surface change vector represented byExpression (3) and the measurement surface similar change vectorrepresented by Expression (6).

The coefficient vector is represented by Expression (8) given below.α*=minimize_(α) ∥E′ _(measure)·α−{tilde over (ε)}∥₂ ²+λ∥α∥₁  (8)

In Expression (8), λ represents a Lagrange multiplier. Expression (8) isused to extract a coefficient vector that minimizes a difference betweenthe measurement surface change vector represented by Expression (3) andthe measurement surface similar change vector represented by Expression(6).

The crack analyzer 1223 estimates the distribution of displacementchanges of the crack occurrence surface based on the coefficient vectorrepresented by Expression (8) and the crack occurrence surface similarchange vector represented by Expression (7).

The distribution of displacement changes of the crack occurrence surfaceis represented by Expression (9) given below.Δ_(crack) _(ans) =Δ′_(crack) _(diff) ·α*  (9)

In Expression (9), the coefficient vector indicating information on theposition and size is multiplied by the crack occurrence surface similarchange vector indicating information on the position and size. Thus, theresult of calculation in Expression (9) indicates the information on theposition and size. As a result, the estimated position and size of thecrack on the crack occurrence surface are acquired.

FIG. 13 is a flow chart for illustrating processing to be executed bythe estimation device of FIG. 1. The processing of from Step S11 to StepS23 is processing to be executed in a learning phase. The processing offrom Step S24 to Step S26 is processing to be executed in an inverseanalysis phase.

In Step S11, the estimator 1 determines whether a condition of learningdata is received. When it is determined by the estimator 1 that thecondition of learning data has not been received, the processing of StepS11 is repeated. The learning data includes a start point of occurrenceand shape model of the crack 55 to be estimated, and a shape of thecrack 55 to be estimated.

On the contrary, in Step S11, when it is determined by the estimator 1that the condition of learning data has been received, the processing ofStep S11 advances to the processing of Step S12.

In Step S12, the estimator 1 determines the start point of occurrence ofthe crack 55. Next, the processing of Step S12 advances to theprocessing of Step S13.

In Step S13, the estimator 1 determines the candidate surface 53. Next,the processing of Step S13 advance to the processing of Step S14.

In Step S14, the estimator 1 determines the observation surface 51.Next, the processing of Step S14 advance to the processing of Step S15.

In Step S15, the estimator 1 generates a shape model. Next, theprocessing of Step S15 advances to the processing of Step S16.

In Step S16, the estimator 1 divides the candidate surface 53 into aplurality of sections A having lattice shapes. Next, the processing ofStep S16 advances to the processing of Step S17.

In Step S17, the estimator 1 sets a plurality of nodes to the pluralityof sections A of the candidate surface 53. Next, the processing of StepS17 advances to the processing of Step S18.

In Step S18, the estimator 1 determines a plurality of patterns ofstructure analysis for which the condition of the crack 55 on thecandidate surface 53 is changed. Next, the processing of Step S18advances to the processing of Step S19.

In Step S19, the estimator 1 determines, for each pattern of structureanalysis, the order of learning the crack 55 at each node. Next, theprocessing of Step S19 advances to the processing of Step S20.

In Step S20, the estimator 1 divides the observation surface 51 into aplurality of sections B having lattice shapes. Next, the processing ofStep S20 advances to the processing of Step S21.

In Step S21, the estimator 1 sets a plurality of nodes to the pluralityof sections B of the observation surface 51. Next, the processing ofStep S21 advances to the processing of Step S22.

In Step S22, the estimator 1 determines, for each pattern of structureanalysis, the order of learning strain of each node on the observationsurface 51. Next, the processing of Step S22 advances to the processingof Step S23.

In Step S23, the estimator 1 executes learning data creation processing.Details of the learning data creation processing are illustrated in FIG.14 and FIG. 15, or in FIG. 19 and FIG. 20. Next, the processing of StepS23 advances to the processing of Step S24.

In Step S24, the estimator 1 acquires measurement data. Next, theprocessing of Step S24 advances to the processing of Step S25.

In Step S25, the estimator 1 executes estimation processing. Details ofthe estimation processing are illustrated in FIG. 16. Next, theprocessing of Step S25 advances to the processing of Step S26.

In Step S26, the estimator 1 executes output processing. Details of theoutput processing are described in each of FIG. 34 and FIG. 35. Next,the processing of Step S26 is finished.

FIG. 14 is a flow chart for illustrating learning data creationprocessing included in the flow chart of FIG. 13.

In Step S31, the estimator 1 acquires information on the start point ofoccurrence of the crack 55. Next, the processing of Step S31 advances tothe processing of Step S32.

In Step S32, the estimator 1 acquires information on the shape model.Next, the processing of Step S32 advances to the processing of Step S33.

In Step S33, the estimator 1 acquires information on the pattern ofstructure analysis. Next, the processing of Step S33 advances to theprocessing of Step S34.

In Step S34, the estimator 1 acquires information on the order oflearning the crack 55. Next, the processing of Step S34 advances to theprocessing of Step S35.

In Step S35, the estimator 1 acquires information on the order oflearning the strain. Next, the processing of Step S35 advances to theprocessing of Step S36.

In Step S36, the estimator 1 creates a structure analysis model. Next,the processing of Step S36 advances to the processing of Step S37.

In Step S37, the estimator 1 determines the crack occurrence surface.Next, the processing of Step S37 advance to the processing of Step S38.

In Step S38, the estimator 1 determines the measurement surface. Next,the processing of Step S38 advance to the processing of Step S39.

In Step S39, the estimator 1 divides the crack occurrence surface into aplurality of sections A having lattice shapes. Next, the processing ofStep S39 advances to the processing of Step S40.

In Step S40, the estimator 1 sets a plurality of nodes to the pluralityof sections A of the crack occurrence surface. Next, the processing ofStep S40 advances to the processing of Step S41.

In Step S41, the estimator 1 divides the measurement surface into aplurality of sections B having lattice shapes. Next, the processing ofStep S41 advances to the processing of Step S42.

In Step S42, the estimator 1 sets a plurality of nodes to the pluralityof sections B of the measurement surface. Next, the processing of StepS42 advances to the processing of Step S43.

In Step S43, the estimator 1 assigns, to the structure analysis model, aboundary condition specifying that no crack 55 has occurred at each nodeon the crack occurrence surface. Next, the processing of Step S43advances to the processing of Step S44.

In Step S44, the estimator 1 calculates the amount of displacementchange of each node on the crack occurrence surface. Next, theprocessing of Step S44 advances to the processing of Step S45.

In Step S45, the estimator 1 calculates strain of each node on themeasurement surface. Next, the processing of Step S45 advances to theprocessing of Step S46.

In Step S46, the estimator 1 assigns, to the structure analysis model, aboundary condition specifying that the node on the crack occurrencesurface is the crack 55. Next, the processing of Step S46 advances tothe processing of Step S47.

In Step S47, the estimator 1 calculates the amount of displacementchange of each node on the crack occurrence surface. Next, theprocessing of Step S47 advances to the processing of Step S48.

In Step S48, the estimator 1 calculates strain of the node on themeasurement surface. Next, the processing of Step S48 advances to theprocessing of Step S49 illustrated in FIG. 15.

FIG. 15 is a flow chart for illustrating processing subsequent to theflow chart of FIG. 14.

In Step S49, the estimator 1 creates a displacement change vectorindicating a difference being an amount of displacement change of a nodeon the crack occurrence surface. Next, the processing of Step S49advances to the processing of Step S50.

In Step S50, the estimator 1 creates a strain change vector indicating adifference being the strain of a node on the measurement surface. Next,the processing of Step S50 advances to the processing of Step S51.

In Step S51, the estimator 1 stores the displacement change vector intothe storage 13. Next, the processing of Step S51 advances to theprocessing of Step S52.

In Step S52, the estimator 1 stores the strain change vector into thestorage 13. Next, the processing of Step S52 advances to the processingof Step S53.

In Step S53, the estimator 1 determines whether structure analysis isperformed for all the nodes on the crack occurrence surface. When theestimator 1 has determined that structure analysis is not performed forall the nodes on the crack occurrence surface, the processing of StepS53 advances to the processing of Step S54.

In Step S54, the estimator 1 changes the node set as the crack 55. Next,the processing of Step S54 returns to the processing of Step S46illustrated in FIG. 14.

On the contrary, when the estimator 1 has determined that structureanalysis is performed for all the nodes on the crack occurrence surface,the processing of Step S53 advances to the processing of Step S55.

In Step S55, the estimator 1 creates a crack surface matrixΔ_(crack_diff) including a displacement change vector. Next, theprocessing of Step S55 advances to the processing of Step S56.

In Step S56, the estimator 1 creates a measurement surface matrixE_(measure) including a strain change vector. Next, the processing ofStep S56 advances to the processing of Step S57.

In Step S57, the estimator 1 generates an estimation model including thecrack surface matrix Δ_(crack_diff) and the measurement surface matrixE_(measure). Next, the processing of Step S57 finishes the learning datacreation processing.

FIG. 16 is a flow chart for illustrating estimation processing includedin the flow chart of FIG. 13.

In Step S71, the estimator 1 reads estimation model information. Next,the processing of Step S71 advances to the processing of Step S72.

In Step S72, the estimator 1 creates a measurement surface change vectorbased on measurement data acquired in Step S24 before starting executionof the estimation processing. Next, the processing of Step S72 advancesto the processing of Step S73.

In Step S73, the estimator 1 acquires information on the measurementsurface estimated change vector. Next, the processing of Step S73advances to the processing of Step S74.

In Step S74, the estimator 1 calculates a cosine similarity between themeasurement surface estimated change vector and the measurement surfacechange vector. Next, the processing of Step S74 advances to theprocessing of Step S75.

In Step S75, the estimator 1 determines whether the cosine similarity ishigher than a reference similarity. When the estimator 1 has determinedthat the cosine similarity is not higher than the reference similarity,the processing of Step S75 advances to the processing of Step S76. Whenthe estimator 1 has determined that the cosine similarity is not higherthan the reference similarity, this means that the estimator 1 hasdetermined that the cosine similarity is equal to or smaller than thereference similarity.

In Step S76, the estimator 1 changes the measurement surface estimatedchange vector. Next, the processing of Step S76 returns to theprocessing of Step S74.

On the contrary, in Step S75, when the estimator 1 has determined thatthe cosine similarity is higher than the reference similarity, theprocessing of Step S75 advances to the processing of Step S77.

In Step S77, the estimator 1 stores the measurement surface estimatedchange vector into the storage 13 as the measurement surface similarchange vector. Next, the processing of Step S77 advances to theprocessing of Step S78.

In Step S78, the estimator 1 stores a crack occurrence surface estimatedchange vector corresponding to the measurement surface similar changevector into the storage 13 as the crack occurrence surface similarchange vector. Next, the processing of Step S78 advances to theprocessing of Step S79.

In Step S79, the estimator 1 determines whether all the measurementsurface estimated change vectors are compared with the measurementsurface change vector. When the estimator 1 has determined that all themeasurement surface estimated change vectors are not compared with themeasurement surface change vector, the processing of Step S79 returns tothe processing of Step S76.

On the contrary, in Step S79, when the estimator 1 has determined thatall the measurement surface estimated change vectors are compared withthe measurement surface change vector, the processing of Step S79advances to the processing of Step S80.

In Step S80, the estimator 1 creates a similarity estimation modelincluding the measurement surface similar change vector and the crackoccurrence surface similar change vector. Next, the processing of StepS80 advances to the processing of Step S81.

In Step S81, the estimator 1 solves a L1-norm minimization problem toextract a coefficient vector based on the measurement surface changevector and the measurement surface similar change vector. The processingof Step S81 advances to the processing of Step S82.

In Step S82, the estimator 1 estimates the distribution of displacementchanges of the crack occurrence surface by multiplying the coefficientvector by the crack occurrence surface similar change vector. Next, theprocessing of Step S82 advances to the processing of Step S83.

In Step S83, the estimator 1 acquires the position and size of the crack55 based on the distribution of displacement changes of the crackoccurrence surface. Next, the processing of Step S83 finishes theestimation processing.

FIG. 17 is a schematic view of a coordinate system at a time when aninternal pressure Pi is applied to an inside of a column member 7serving as the structure to be subjected to estimation by the estimationdevice of FIG. 1. In FIG. 17, the column member 7 is represented in acylindrical coordinate system.

FIG. 18 is a plan view of the column member 7 of FIG. 17. As illustratedin FIG. 18, the inner pressure Pi is applied to the inner peripheralsurface 7 i of the column member 7 at the time of shrink-fitting. As aresult, the shape of an outer peripheral surface 7 o of the columnmember 7 changes due to occurrence of the crack 55 inside the columnmember 7. Through shrink-fitting, the column member 7 is mounted to aholding ring of a rotator core that protrudes at an end of a rotator ofthe rotary electric machine, for example.

According to the above description, in the first embodiment, theestimation device includes the measurement device 3 and the estimator 1.The measurement device 3 is configured to set the observation surface 51on the surface of a structure as the measurement surface to measure thechange of the measurement surface as the measurement surface changevector. The estimator 1 is configured to solve the norm minimizationproblem to acquire a coefficient vector by setting, as parameters, themeasurement surface change vector and a part of the estimation modelgenerated based on a shape model that models the shape of the structure.The coefficient vector forms the sparse solution. The estimator 1 isconfigured to use the coefficient vector and another part of theestimation model to estimate the change of the crack occurrence surfaceby setting, as the crack occurrence surface, the candidate surface 53,which is inside the structure and assumed to have the crack 55.

With the configuration described above, when the distribution ofdisplacement changes of the crack occurrence surface is estimated, thefeature of the measurement surface change vector is extracted as acoefficient vector. This coefficient vector forms a sparse solution. Asa result, this coefficient vector is a small number of non-zeroelements. Thus, uniqueness of the solution, existence of the solution,and stability of the solution are satisfied, and it is possible toaccurately estimate the crack 55 inside the structure.

Further, the estimator 1 includes the model generator 11, the similarvector extractor 1221, the feature extractor 1222, and the crackanalyzer 1223.

The model generator 11 is configured to perform structure analysis basedon the boundary condition set in advance for the shape model generatedbased on the measurement surface and the crack occurrence surface. Themodel generator 11 is configured to perform structure analysis togenerate a plurality of measurement surface estimated change vectorseach estimating a change of the measurement surface. The model generator11 is configured to perform structure analysis to generate, as a changeof the crack occurrence surface, a plurality of crack occurrence surfaceestimated change vectors each estimating a displacement change of thecrack occurrence surface. The model generator 11 is configured togenerate an estimation model including a measurement surface estimatedchange vector and a crack occurrence surface estimated change vector.

The similar vector extractor 1221 is configured to set, as themeasurement surface similar change vector, a measurement surfaceestimated change vector whose similarity with the measurement surfacechange vector is higher than the reference similarity set in advance.The similar vector extractor 1221 is configured to set, as the crackoccurrence surface similar change vector, a crack occurrence surfaceestimated change vector corresponding to the measurement surface similarchange vector. The similar vector extractor 1221 is configured toextract the measurement surface similar change vector serving as a partof the estimation model and a crack occurrence surface similar changevector serving as another part of the estimation model.

The feature extractor 1222 is configured to solve a L1-norm minimizationproblem, which is a norm minimization problem, to extract a coefficientvector based on the measurement surface change vector and themeasurement surface similar change vector.

The crack analyzer 1223 is configured to estimate the change ofdistribution of displacement changes of the crack occurrence surfacebased on the coefficient vector and the crack occurrence surface similarchange vector.

With the configuration described above, a measurement surface estimatedchange vector having a high similarity is used as the measurementsurface similar change vector among measurement surface estimated changevectors, and thus it is possible to execute estimation processing byscreening learning data. Thus, the result of estimating the distributionof displacement changes of the crack occurrence surface becomesaccurate.

Further, the similar vector extractor 1221 is configured to use a cosinesimilarity as the similarity.

Thus, it is possible to shorten the period of time for calculating thesimilarity.

Further, the model generator 11 is configured to divide the crackoccurrence surface into the plurality of sections A, and set a pluralityof nodes forming the respective sections A as the crack 55, and estimatethe displacement change of each node as each crack occurrence surfaceestimated change vector.

With the configuration described above, learning data is limited, andthus it is possible to shorten the period of time for generating anestimation model.

Further, the model generator 11 is configured to model the shape modelas a model in a cylindrical coordinate system.

With the configuration described above, even when the structure has acolumn shape, it is possible to accurately estimate the crack 55 insidethe structure.

Second Embodiment

In a second embodiment of this disclosure, description of configurationsand functions that are the same as or equivalent to those of the firstembodiment is omitted. A node to be identified as the crack 55 in thesecond embodiment is different from a node identified as the crack 55 inthe first embodiment. Other configurations of the second embodiment aresimilar to those of the first embodiment. In other words, otherconfigurations of the second embodiment are configurations and functionsthat are the same as or equivalent to those of the first embodiment.Thus, other configurations of the second embodiment are denoted by thesame reference symbols as those of the first embodiment.

FIG. 19 is a flow chart for illustrating learning data creationprocessing in the second embodiment. The processing of from Step S101 toStep S115, and the processing of Step S117 and Step S118 are similar tothe processing of from Step S31 to Step S45, and the processing of StepS47 and Step S48, respectively. Thus, description thereof is omittedhere. The processing of Step S116 is processing different from that ofthe first embodiment. In Step S116, the estimator 1 executes boundarycondition assignment processing. Details of the boundary conditionassignment processing are illustrated in FIG. 25.

FIG. 20 is a flow chart for illustrating processing subsequent to theflow chart of FIG. 19. The processing of from Step S119 to Step S127 issimilar to the processing of from Step S49 to Step S57 of FIG. 15 exceptfor processing that is executed subsequent to the processing of StepS124. Thus, description thereof is omitted here. After the estimator 1has executed the processing of Step S124, the processing of Step S124returns to the processing of Step S116.

FIG. 21 is a diagram for illustrating an example of a side C of a crackoccurrence surface determined at a time of the boundary conditionassignment processing of FIG. 19.

FIG. 22 is a diagram for illustrating a first example of a nodeidentified as the crack 55 on the crack occurrence surface determined atthe time of the boundary condition assignment processing of FIG. 19. InFIG. 22, a node at the position (0, 0) on the side C is identified asthe crack 55. As a result, the crack 55 is set at the position of (0,0). The measurement surface estimated change vector is calculated byperforming structure analysis under such a boundary condition.

FIG. 23 is a diagram for illustrating a second example of the nodeidentified as the crack 55 on the crack occurrence surface determined atthe time of the boundary condition assignment processing of FIG. 19. InFIG. 23, a node at the position of (3, 2) is identified as the crack 55.In this case, the crack 55 is at a position other than the side C. Thus,all the nodes included in a perpendicular line that extends from thenode to the line C are determined as the crack 55. AS a result, thecrack 55 is set at the positions of (3, 2), (3, 1), and (3, 0). Themeasurement surface estimated change vector is calculated by performingstructure analysis under such a boundary condition.

FIG. 24 is a diagram for illustrating a third example of the nodeidentified as the crack 55 on the crack occurrence surface determined atthe time of the boundary condition assignment processing of FIG. 19. InFIG. 24, a node at the position of (n−2, 4) is identified as the crack55. In this case, the crack 55 is at a position other than the side C.Thus, all the nodes included in a perpendicular line that extends fromthe node to the line C are determined as the crack 55. AS a result, thecrack 55 is set at the positions of (n−2, 4), (n−2, 3), (n−2, 2), (n−2,1), and (n−2, 0). The measurement surface estimated change vector iscalculated by performing structure analysis under such a boundarycondition.

FIG. 25 is a flow chart for illustrating the boundary conditionassignment processing of FIG. 19.

In Step S131, the estimator 1 determines the side C of the crackoccurrence surface. Next, the processing of Step S131 advances to theprocessing of Step S132.

In Step S132, the estimator 1 determines a node on the side C of thecrack occurrence surface. Next, the processing of Step S132 advances tothe processing of Step S133.

In Step S133, the estimator 1 identifies a node to be included in aperpendicular line of the side C, which starts from the determined node.Next, the processing of Step S133 advances to the processing of StepS134.

In Step S134, the estimator 1 assigns, to the structure analysis model,a boundary condition setting the identified node as the crack 55. Next,the processing of Step S134 finishes the boundary condition assignmentprocessing.

According to the above description, in the second embodiment, the modelgenerator 11 is configured to focus on displacement changes of aplurality of nodes forming the plurality of sections A, which arecontinuously adjacent to one another in a part of the region on thecrack occurrence surface, among the sections A.

With the configuration described above, it is possible to limit learningdata by narrowing down nodes identified as the crack 55. Therefore, itis possible to suppress reduction of the accuracy of estimating thechange of the crack occurrence surface due to overtraining.

Third Embodiment

In a third embodiment of this disclosure, description of configurationsand functions that are the same as or equivalent to those of the firstand second embodiments is omitted. The measurement surface matrixDis_(measure) and the measurement surface matrix A_(measure) in thethird embodiment are different from the measurement surface matrixE_(measure) in the first and second embodiments. Other configurations ofthe third embodiment are similar to those of the first and secondembodiments. That is, other configurations of the third embodiment areconfigurations and functions that are the same as or equivalent to thoseof the first and second embodiments. Thus, other configurations of thethird embodiment are denoted by the same reference symbols as those ofthe first and second embodiments.

FIG. 26 is a diagram for illustrating a displacement change vectorindicating a displacement change of each node on the observation surface51 for each position of the crack 55 on the candidate surface 53 in thethird embodiment. As illustrated in FIG. 26, pieces of displacement dataon respective nodes included in a column vector of Dis(-, -) arearranged in an order of moving the crack 55 assumed for each of thosenodes. A position d(i, j) represents a displacement change of a node ata position (i, j) on the observation surface 51 of FIG. 7. Further, theposition of the crack 55 at the time of structure analysis is assignedto this column vector, and the position of the crack 55 at the time ofstructure analysis is assigned to displacement data on each nodeincluded in this column vector.

FIG. 27 is a diagram for illustrating a measurement surface matrixDis_(measure) including the plurality of displacement change vectors ofFIG. 26. Each of the plurality of displacement change vectors of FIG. 26includes a column vector. The measurement surface matrix Dis_(measure)of FIG. 27 is acquired by arranging those column vectors in the order ofmoving the crack 55 assumed for each node. The measurement surfacematrix Dis_(measure) is represented by Expression (10) given below.

$\begin{matrix}{{Dis}_{measure} = \begin{pmatrix}{d_{0,0}\left( {0,} \right)} & \ldots & {d_{i,j}\left( {0,0} \right)} & \ldots & {d_{n,m}\left( {0,0} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{d_{0,0}\left( {k,l} \right)} & \ldots & {d_{i,j}\left( {k,l} \right)} & \ldots & {d_{n,m}\left( {k,l} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{d_{0,0}\left( {n,p} \right)} & \ldots & {d_{i,j}\left( {n,p} \right)} & \ldots & {d_{n,m}\left( {n,p} \right)}\end{pmatrix}} & (10)\end{matrix}$

Further, when the displacement change of each node on the observationsurface 51 is measured, the measurement device 3 includes at least adisplacement sensor. The displacement sensor includes, for example, alaser displacement sensor, an eddy current loss type displacementsensor, a capacitance type displacement sensor, a contact typedisplacement sensor, a wire type displacement sensor, or a lasermicrometer.

Further, the displacement measurement method may include opticalmeasurement such as speckle interferometry, moire interferometry, or adigital image correlation method.

FIG. 28 is a diagram for illustrating an angle change vector indicatingan angle of each node on the observation surface 51 for each position ofthe crack 55 on the candidate surface 53 in the third embodiment. Asillustrated in FIG. 28, pieces of angle data on respective nodesincluded in a column vector of A(-, -) are arranged in an order ofmoving the crack assumed for each of those nodes. A position A(i, j)represents an angle change of a node at a position (i, j) on theobservation surface 51 of FIG. 7. Further, the position of the crack 55at the time of structure analysis is assigned to this column vector, andthe position of the crack 55 at the time of structure analysis isassigned to angle data on each node included in this column vector.

FIG. 29 is a diagram for illustrating a measurement surface matrixA_(measure) including the plurality of angle change vectors of FIG. 28.Each of the plurality of angle change vectors of FIG. include a columnvector. The measurement surface matrix A_(measure) of FIG. 29 isacquired by arranging those column vectors in the order of moving thecrack 55 assumed for each node. The measurement surface matrixA_(measure) is represented by Expression (11) given below.

$\begin{matrix}{A_{measure} = \begin{pmatrix}{a_{0,0}\left( {0,} \right)} & \ldots & {a_{i,j}\left( {0,0} \right)} & \ldots & {a_{n,m}\left( {0,0} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{a_{0,0}\left( {k,l} \right)} & \ldots & {a_{i,j}\left( {k,l} \right)} & \ldots & {a_{n,m}\left( {k,l} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{a_{0,0}\left( {n,p} \right)} & \ldots & {a_{i,j}\left( {n,p} \right)} & \ldots & {a_{n,m}\left( {n,p} \right)}\end{pmatrix}} & (11)\end{matrix}$

Further, when the angle of each node on the observation surface 51 ismeasured, the measurement device 3 includes at least an inclinationsensor. That is, the inclination angle of each node on the observationsurface 51 is measured by the inclination sensor. Further, the principleof an optical lever may be used to measure the angle of a node on theobservation surface 51.

According to the above description, in the third embodiment, themeasurement device 3 is configured to measure, as the change of themeasurement surface, at least one of the displacement change, the strainchange, or the angle change of the measurement surface.

With the configuration described above, at least one of the strainchange, the displacement change, or the angle change can be used as thechange of the measurement surface in a structure, and thus it ispossible to increase the type of measurement methods.

Further, it is possible to measure the change of the observation surface51 in a structure in a shorter period of time and more accurately thanmeasurement of strain by using not a strain change but a displacementchange or an angle change as the change of the observation surface 51 inthe structure.

Fourth Embodiment

In a fourth embodiment of this disclosure, description of configurationsand functions that are the same as or equivalent to those of the firstto third embodiments is omitted. The crack surface matrix Z_(crack_diff)in the fourth embodiment are different from the crack surface matrixΔ_(crack_diff) in the first to third embodiments. Other configurationsof the fourth embodiment are similar to those of the first to thirdembodiments. That is, other configurations of the fourth embodiment areconfigurations and functions that are the same as or equivalent to thoseof the first to third embodiments. Thus, other configurations of thefourth embodiment are denoted by the same reference symbols as those ofthe first to third embodiments.

FIG. 30 is a diagram for illustrating a load change vector indicating aload of each node for each position of the crack 55 on the candidatesurface 53 in the fourth embodiment. As illustrated in FIG. 30, piecesof load data on respective nodes included in a column vector of Z(-, -)are arranged in an order of moving the crack 55 assumed for each ofthose nodes. A position ξ(i, j) represents a load of a node at aposition (i, j) on the candidate surface 53 of FIG. 5. Further, theposition of the crack 55 at the time of structure analysis is assignedto this column vector, and the position of the crack 55 at the time ofstructure analysis is assigned to load data on each node included inthis column vector.

FIG. 31 is a diagram for illustrating a crack surface matrixZ_(crack_diff) including the plurality of load change vectors of FIG.30. Each of the plurality of load change vectors of FIG. 30 includes acolumn vector. The crack surface matrix Z_(crack_diff) of FIG. 31 isacquired by arranging those column vectors in the order of moving thecrack 55 assumed for each node. The crack surface matrix Z_(crack_diff)is represented by Expression (12) given below.

$\begin{matrix}{Z_{{crack}\_{diff}} = \begin{pmatrix}{\zeta_{0,0}\left( {0,} \right)} & \ldots & {\zeta_{i,j}\left( {0,0} \right)} & \ldots & {\zeta_{n,m}\left( {0,0} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{\zeta_{0,0}\left( {i,l} \right)} & \ldots & {\zeta_{i,j}\left( {i,j} \right)} & \ldots & {\zeta_{n,m}\left( {i,j} \right)} \\\vdots & \ddots & \vdots & \ddots & \vdots \\{\zeta_{0,0}\left( {n,m} \right)} & \ldots & {\zeta_{i,j}\left( {n,m} \right)} & \ldots & {\zeta_{n,m}\left( {n,m} \right)}\end{pmatrix}} & (12)\end{matrix}$

Specifically, the force of a node at a position of the crack 55 is setto 0, and the force of a node at a position without the crack 55 is setto a value other than 0. Specifically, a force is generated at a nodeother than the position of the crack 55, and thus it is possible toaccurately estimate the crack 55 inside a structure also by using nodeload data.

According to the above description, in the fourth embodiment, the modelgenerator 11 is configured to estimate a change in load of the crackoccurrence surface as the change of the crack occurrence surface.

With the configuration described above, the crack surface matrixZ_(crack_diff) can include load change information, and thus it ispossible to estimate the crack 55 accurately in terms of variousaspects.

Fifth Embodiment

In a fifth embodiment of this disclosure, description of configurationsand functions that are the same as or equivalent to those of the firstto fourth embodiments is omitted. In the fifth embodiment, theconfiguration of applying a load to a structure for inspection isdifferent from those of the first to fourth embodiments. Otherconfigurations of the fifth embodiment are similar to those of the firstto fourth embodiments. That is, other configurations of the fifthembodiment are configurations and functions that are the same as orequivalent to those of the first to fourth embodiments. Thus, otherconfigurations of the fifth embodiment are denoted by the same referencesymbols as those of the first to fourth embodiments.

FIG. 32 is a block diagram for illustrating an example of aconfiguration of an estimation device according to the fifth embodiment.As illustrated in FIG. 32, the shape model generator 111 includes a loadsetter 1111. The data acquisition module 121 includes a load instructor1211. The load setter 1111 is configured to specify a size of a load tobe applied to a structure, and a position of the structure at which theload is applied. The load instructor 1211 is configured to apply a loadto a structure based on an instruction from the load setter 1111. Withthis, it is possible to measure the change of the observation surface 51under a state in which a load is applied to a structure.

FIG. 33 is a flow chart for illustrating processing to be executed bythe estimation device of FIG. 32. The processing of from Step S141 toStep S144 is processing to be executed in a learning phase. Theprocessing of from Step S151 to Step S155 is processing to be executedin an inverse analysis phase.

In Step S141, the estimator 1 determines an inspection target. Theprocessing of Step S141 is processing that corresponds to the processingof from Step S11 to Step S14 of FIG. 13. Next, the processing of StepS141 advances to the processing of Step S142.

In Step S142, the estimator 1 sets an inspection load. The processing ofStep S142 is processing that does not exist in the processing of fromStep S11 to Step S26 of FIG. 13. Next, the processing of Step S142advances to the processing of Step S143.

In Step S143, the estimator 1 generates a shape model. The processing ofStep S143 corresponds to the processing of Step S15 of FIG. 13. Next,the processing of Step S143 advances to the processing of Step S144.

In Step S144, the estimator 1 generates an estimation model. Theprocessing of Step S144 is processing that corresponds to the processingof from Step S16 to Step S23 of FIG. 13. Next, the processing of StepS144 finishes the learning phase.

In Step S151, the estimator 1 specifies the inspection load set in thelearning phase. The processing of Step S151 is processing that does notexist in the processing of from Step S11 to Step S26 of FIG. 13. Next,the processing of Step S151 advances to the processing of Step S152.

In Step S152, the estimator 1 acquires measurement data. The processingof Step S152 is processing that corresponds to the processing of StepS24 of FIG. 13. Next, the processing of Step S152 advances to theprocessing of Step S153.

In Step S153, the estimator 1 reads information on the estimation modelgenerated in the learning phase. The processing of Step S153 isprocessing that corresponds to the processing of Step S71 of FIG. 16.Next, the processing of Step S153 advances to the processing of StepS154.

In Step S154, the estimator 1 estimates a crack state. The processing ofStep S154 is processing that corresponds to the processing of from StepS72 to Step S83 of FIG. 16. Next, the processing of Step S154 advancesto the processing of Step S155.

That is, the processing of Step S153 and the processing of Step S154 areprocessing that corresponds to the processing of Step S25 of FIG. 13.

In Step S155, the estimator 1 executes output processing. The processingof Step S155 is processing that corresponds to the processing of StepS26 of FIG. 13. Next, the processing of Step S155 finishes the crackstate analysis phase.

According to the above description, in the fifth embodiment, themeasurement device 3 is configured to perform the measurement under astate in which a load is applied to the structure before the generationof the shape model.

With the configuration described above, a load can be applied to astructure at the time of inspecting the structure, and thus it is alsopossible to inspect a structure to which a load is not applied inadvance. Therefore, it is possible to increase the number of structuresthat can be inspected.

Sixth Embodiment

In a sixth embodiment of this disclosure, description of configurationsand functions that are the same as or equivalent to those of the firstto fifth embodiments is omitted. The sixth embodiment is different fromthe first to fifth embodiments in terms of configuration in that detailsof the output processing are described. Other configurations of thesixth embodiment are similar to those of the first to fifth embodiments.That is, other configurations of the sixth embodiment are configurationsand functions that are the same as or equivalent to those of the firstto fifth embodiments. Thus, other configurations of the sixth embodimentare denoted by the same reference symbols as those of the first to fifthembodiments.

The analysis result output module 14 is configured to acquireinformation on the position of the crack 55, information on the size ofthe crack 55, information on a load applied to a structure, a physicalproperty value of the structure, information on the size of the crack 55that disables usage of the structure, and information on the position ofthe crack 55 that disables usage of the structure. In this case, thephysical property value is a modulus of rigidity, for example.

The information on the position of the crack 55, the information on thesize of the crack 55, and the information on a load applied to astructure can be acquired from the crack state analyzer 12. The physicalproperty value of the structure, the information on the size of thecrack 55 that disables usage of the structure, and the information onthe position of the crack 55 that disables usage of the structure can beacquired from the storage 13. The information on the size of the crack55 that disables usage of the structure and the information on theposition of the crack 55 that disables usage of the structure are usedas limit values.

In this case, various kinds of information stored in the storage 13store information established at the stage of designing a product.

The analysis result output module 14 determines a remaining usage periodbased on the limit values and the amount of expansion of the crack 55acquired based on those pieces of information.

The remaining usage period is determined based on the position of thecrack 55, the size of the crack 55, the physical property value of thestructure, the load applied to the structure, and a usage condition ofthe structure known in fracture mechanics. Further, the remaining usageperiod may be estimated based on chronological change of the size andposition of the crack 55.

FIG. 34 is a flow chart for illustrating the output processing of FIG.13 and FIG. 33 in the sixth embodiment.

In Step S161, the estimator 1 acquires the information on the positionand size of the crack 55. Next, the processing of Step S161 advances tothe processing of Step S162.

In Step S162, the estimator 1 acquires the information on a load appliedto a structure. Next, the processing of Step S162 advances to theprocessing of Step S163.

In Step S163, the estimator 1 acquires information on the physicalproperty value of the structure. Next, the processing of Step S163advances to the processing of Step S164.

In Step S164, the estimator 1 acquires the information on the size andposition of the crack 55 that disable usage of the structure as thelimit values. Next, the processing of Step S164 advances to theprocessing of Step S165.

In Step S165, the estimator 1 acquires the amount of expansion of thecrack 55 on the crack occurrence surface based on the position and sizeof the crack 55, the load applied to the structure, and the physicalproperty value of the structure. Next, the processing of Step S165advances to the processing of Step S166.

In Step S166, the estimator 1 determines the remaining usage periodbased on the amount of expansion of the crack 55 and the limit values.Next, the processing of Step S166 advances to the processing of StepS167.

In Step S167, the estimator 1 outputs the information on the determinedremaining usage period. Next, the processing of Step S167 finishes theoutput processing.

According to the above description, in the sixth embodiment, first, theamount of expansion of the crack 55 on the crack occurrence surface isacquired based on the load applied to the structure and the physicalvalue of the structure. Next, the remaining usage period of thestructure is determined based on the amount of expansion of the crack 55and the size and position of the crack 55 in the structure.

With the configuration described above, the remaining usage period isoutput based on the information on the estimated position and size ofthe crack 55, and thus it is possible to create a plan of operating thestructure more specifically. For example, a period in which thestructure is to be repaired and a period in which the structure is to beupdated are clarified in advance, and thus it is possible to repair andupdate the structure in accordance with a plan.

Seventh Embodiment

In a seventh embodiment of this disclosure, description ofconfigurations and functions that are the same as or equivalent to thoseof the first to sixth embodiments is omitted. The output processing inthe seventh embodiment is different from the configuration in the firstto sixth embodiments. Other configurations of the seventh embodiment aresimilar to those of the first to sixth embodiments. That is, otherconfigurations of the seventh embodiment are configurations andfunctions that are the same as or equivalent to those of the first tosixth embodiments. Thus, other configurations of the seventh embodimentare denoted by the same reference symbols as those of the first to sixthembodiments.

The analysis result output module 14 acquires the information on theposition and size of the crack 55 estimated by the crack analyzer 1223.The analysis result output module 14 acquires the information on theposition and size of the crack 55 that disable usage of the structurestored in the storage 13 as limit values. When the estimated positionand size of the crack 55 each exceed the limit value, the analysisresult output module 14 generates an alarm urging stop of usage of thestructure. Further, when the information on the remaining usage periodcan be acquired, the analysis result output module 14 notifies of theremaining usage period.

The alarm and notification are executed by using a sound, a character,flashing, or lighting, for example. The analysis result output module 14includes at least one of a speaker, a liquid crystal display, or a lightemitting device.

For example, when the analysis result output module 14 includes aspeaker, the analysis result output module 14 can output an alarm ornotification by a sound. Further, when the analysis result output module14 includes a liquid crystal display, the analysis result output module14 can output an alarm or notification by characters. Further, when theanalysis result output module 14 includes a light emitting device, theanalysis result output module 14 can output an alarm or notification byflashing or lighting.

FIG. 35 is a flow chart for illustrating the output processing of FIG.13 and FIG. 33 in the seventh embodiment.

In Step S181, the estimator 1 acquires the information on the positionand size of the crack 55. Next, the processing of Step S181 advances tothe processing of Step S182.

In Step S182, the estimator 1 notifies of the position and size of thecrack 55. Next, the processing of Step S182 advances to the processingof Step S183.

In Step S183, the estimator 1 acquires the information on a load appliedto a structure. Next, the processing of Step S183 advances to theprocessing of Step S184.

In Step S184, the estimator 1 acquires information on the physicalproperty value of the structure. Next, the processing of Step S184advances to the processing of Step S185.

In Step S185, the estimator 1 acquires the information on the positionand size of the crack 55 that disable usage of the structure as thelimit values. Next, the processing of Step S185 advances to theprocessing of Step S186.

In Step S186, the estimator 1 determines whether the position and sizeof the crack 55 each exceed the limit value. When the estimator 1 hasdetermined that the position and size of the crack 55 each exceed thelimit value, the processing of Step S186 advances to the processing ofStep S187.

In Step S187, the estimator 1 generates an alarm urging stop of usage ofthe structure. Next, the processing of Step S187 finishes the outputprocessing.

On the contrary, in Step S186, when the estimator 1 has determined thatthe position and size of the crack 55 do not each exceed the limitvalue, the processing of Step S186 advances to the processing of StepS188. In this case, when the estimator 1 has determined that theposition and size of the crack 55 do not each exceed the limit value,this means that the estimator 1 has determined that the position andsize of the crack 55 are each equal to or smaller than the limit value.

In Step S188, the estimator 1 notifies of the fact that the crack 55exists. Next, the processing of Step S188 advances to the processing ofStep S189.

In Step S189, the estimator 1 determines whether the information on theremaining usage period can be acquired. When the estimator 1 hasdetermined that the information on the remaining usage period can beacquired, the processing of Step S189 advances to the processing of StepS190.

In Step S190, the estimator 1 acquires the information on the remainingusage period. Next, the processing of Step S190 advances to theprocessing of Step S191.

In Step S191, the estimator 1 notifies of the remaining usage period.Next, the processing of Step S191 finishes the output processing.

On the contrary, in Step S189, when the estimator 1 has determined thatthe information on the remaining usage period cannot be acquired, theprocessing of Step S189 finishes the output processing.

According to the above description, in the seventh embodiment, thenotification urging stop of usage of the structure is output based onthe amount of expansion of the crack 55 and the size and position of thecrack 55 in the structure.

With the configuration described above, an operator of the structure canquickly determine to stop usage of the structure.

Eighth Embodiment

In an eighth embodiment of this disclosure, description ofconfigurations and functions that are the same as or equivalent to thoseof the first to seventh embodiments is omitted. The candidate surface 53determined in the eighth embodiment is different from the configurationof the first to seventh embodiments. Other configurations of the eighthembodiment are similar to those of the first to seventh embodiments.That is, other configurations of the eighth embodiment areconfigurations and functions that are the same as or equivalent to thoseof the first to seventh embodiments. Thus, other configurations of theeighth embodiment are denoted by the same reference symbols as those ofthe first to seventh embodiments.

FIG. 36 is a diagram for illustrating the candidate surface 53 in theeighth embodiment. As illustrated in FIG. 36, when a point having themaximum stress is measured or acquired by structure analysis as themaximum stress σ_(max), the model generator identifies the point havingthe maximum stress as an occurrence location of the crack 55. The modelgenerator 11 determines, as the candidate surface 53, a surface that isperpendicular to the stress at the identified occurrence location andpenetrates through a surface opposing the identified occurrencelocation.

FIG. 37 is a flow chart for illustrating processing of determining thecandidate surface 53 of FIG. 36.

In Step S201, the estimator 1 determines whether the distribution ofstress that occurs in the structure is acquired. When the estimator 1has determined that the distribution of stress that occurs in thestructure is not acquired, the processing of Step S201 is repeated.

On the contrary, in Step S201, when the estimator 1 has determined thatthe distribution of stress that occurs in the structure is acquired, theprocessing of Step S201 advances to the processing of Step S202.

In Step S202, the estimator 1 determines whether there is a point havingthe maximum stress. When the estimator 1 has determined that there is nopoint having the maximum stress, the processing of Step S202 isrepeated.

On the contrary, in Step S202, when the estimator 1 has determined thatthere is a point having the maximum stress, the processing of Step S202advances to the processing of Step S203.

In Step S203, the estimator 1 identifies a point having the maximumstress as the occurrence location of the crack 55. Next, the processingof Step S203 advances to the processing of Step S204.

In Step S204, the estimator 1 determines, as the candidate surface 53, asurface that is perpendicular to the stress at the identified occurrencelocation of the crack 55 and penetrates through a surface opposing theidentified occurrence location of the crack 55. Next, the processing ofStep S204 finishes the processing.

According to the above description, in the eighth embodiment, a pointhaving the maximum stress on the crack occurrence surface, which occursdepending on the boundary condition, is identified as the occurrencelocation of the crack 55.

With the configuration described above, it is possible to determine, asthe candidate surface 53, a surface on which the crack 55 is liable tooccur inside the structure. Therefore, it is possible to further improvethe accuracy of estimating the crack 55.

Further, in each embodiment, a processing circuit for executing theestimation device is included. The processing circuit may be constructedby dedicated hardware or a central processing unit (CPU, which is alsoreferred to as “processing unit”, “arithmetic unit”, “microprocessor”,“microcomputer”, “processor”, or “DSP”) for executing a program storedin a memory.

FIG. 38 is a diagram for illustrating an example of a hardwareconfiguration. In FIG. 38, a processing circuit 401 is connected to abus 402. When the processing circuit 401 is dedicated hardware, theprocessing circuit 401 is, for example, a single circuit, a compositecircuit, a programmed processor, an ASIC, an FPGA, or a combinationthereof. Each function of the components of the estimation device may beimplemented by the processing circuit 401, or the functions of thosecomponents may be collectively implemented by the processing circuit401.

FIG. 39 is a diagram for illustrating another example of the hardwareconfiguration. In FIG. 39, a processor 403 and a memory 404 areconnected to the bus 402. When the processing circuit is a CPU, thefunction of each unit of the estimation device is implemented by acombination of software, firmware, or a combination of software andfirmware. The software or firmware is described as a program, and isstored into the memory 404. The processing circuit reads out a programstored in the memory 404 and executes the program to implement thefunction of each component. That is, the estimation device includes thememory 404 for storing a program to be executed by the processingcircuit so that steps are consequently executed. Further, those programscan be regarded as programs for causing a computer to execute aprocedure or method to be executed. In this case, the memory 404 is avolatile or non-volatile semiconductor memory such as a RAM, a ROM, aflash memory, an EPROM, or an EEPROM, or is a magnetic disk, a flexibledisk, an optical disc, a compact disc, a mini disc, or a DVD.

A part of the function of each component of the estimation device may beimplemented by dedicated hardware, and another part of the function ofeach component may be implemented by software or firmware. For example,a processing circuit being dedicated hardware can implement the modelgenerator 11 among the functions. Further, a processing circuit can readout a program stored in the memory 404 and execute the program toimplement the crack analyzer 1223 among the functions.

In this way, the processing circuit can implement each of theabove-mentioned functions by hardware, software, firmware, or acombination thereof.

In the first to eighth embodiments, description has been made of anexample in which the candidate surface 53 is divided into lattices to beset as the sections A, and the observation surface 51 is divided intolattices to be set as the elements B. However, this disclosure is notparticularly limited thereto. For example, the candidate surface 53 maybe divided into trapezoid shapes to be set as the sections A, and theobservation surface 51 may be divided into trapezoid shapes to be set asthe sections B.

Further, in the first embodiment, description has been given of anexample of using a strain gauge as the measurement device 3. However,this disclosure is not particularly limited thereto. For example, anoptical device, for example, a digital camera, and a device that hasinstalled therein software for analyzing image information acquired bythe optical device may be used as the measurement device 3. In thiscase, this device executes an image analysis algorithm by a digitalimage correlation method to measure strain of the surface of a structurein a non-contact manner.

Further, in the eighth embodiment, description has been given of anexample in which the maximum stress σ_(max) is determined as a result ofmeasurement or structure analysis. However, the maximum stress σ_(max)is likely to occur at a location to which a boundary condition is set.Thus, setting of a boundary condition may be reconsidered by focusing onthe occurrence location of the maximum stress σ_(max).

What is claimed is:
 1. An estimation device to estimate a state of acrack on a physical structure within an electric machine, the estimationdevice comprising: a measurement device configured to set an observationsurface on a surface of the physical structure within the electricmachine as a measurement surface to measure a change of the measurementsurface as a measurement surface change vector; and an estimator,implemented by processing circuitry, configured to estimate the crack onthe physical structure within the electric machine based on a change ofthe measurement surface measured by the measurement device, byestimating a change of a crack occurrence surface by determining acandidate surface, which is inside the physical structure within theelectric machine and assumed to have the crack, as the crack occurrencesurface, based on: a coefficient vector forming a sparse solutionacquired by solving a norm minimization problem by setting, asparameters, the measurement surface change vector and a part of anestimation model, which is generated from a shape model obtained bymodeling a shape of the physical structure within the electric machine;and another part of the estimation model.
 2. The estimation deviceaccording to claim 1, wherein the estimation device includes: a modelgenerator configured to perform structure analysis based on a boundarycondition set in advance for the shape model generated based on themeasurement surface and the crack occurrence surface, to therebygenerate the estimation model including a plurality of measurementsurface estimated change vectors each estimating a change of themeasurement surface and a plurality of crack occurrence surfaceestimated change vectors each estimating a displacement change of thecrack occurrence surface as the change of the crack occurrence surface;a similar vector extractor configured to: set, as a measurement surfacesimilar change vector, a measurement surface estimated change vectorhaving a similarity with the measurement surface change vector higherthan a reference similarity set in advance; set, as a crack occurrencesurface similar change vector, a crack occurrence surface estimatedchange vector corresponding to the measurement surface similar changevector; and extract the measurement surface similar change vectorserving as the part of the estimation model and the crack occurrencesurface similar change vector serving as the another part of theestimation model; a feature extractor configured to extract thecoefficient vector by solving an L1 norm minimization problem as thenorm minimization problem based on the measurement surface change vectorand the measurement surface similar change vector; and a crack analyzerconfigured to estimate a change in distribution of displacement changesof the crack occurrence surface based on the coefficient vector and thecrack occurrence surface similar change vector.
 3. The estimation deviceaccording to claim 2, wherein the similar vector extractor is configuredto use a cosine similarity as the similarity.
 4. The estimation deviceaccording to claim 2, wherein the model generator is configured to:divide the crack occurrence surface into a plurality of sections; set aplurality of nodes forming the respective plurality of sections as thecrack; and estimate a displacement change of each of the plurality ofnodes as the crack occurrence surface estimated change vector.
 5. Theestimation device according to claim 4, wherein the model generator isconfigured to focus on displacement changes of a plurality of nodesforming a plurality of sections, which are continuously adjacent to oneanother in a partial region of the crack occurrence surface among theplurality of sections.
 6. The estimation device according to claim 2,wherein the model generator is configured to model the shape model as amodel in a cylindrical coordinate system.
 7. The estimation deviceaccording to claim 2, wherein the measurement device is configured toperform the measurement under a state in which a load is applied to thephysical structure within the electric machine before the generation ofthe shape model.
 8. The estimation device according to claim 2, whereinthe model generator is configured to estimate a change in load of thecrack occurrence surface as the change of the crack occurrence surface.9. The estimation device according to claim 1, wherein the measurementdevice is configured to measure, as the change of the measurementsurface, at least one of a displacement change, a strain change, or anangle change of the measurement surface.
 10. An estimation method toestimate a state of a crack on a physical structure within an electricmachine, the estimation method comprising: setting, by a measurementdevice, an observation surface on a surface of the physical structurewithin the electric machine as a measurement surface to measure a changeof the measurement surface as a measurement surface change vector; andestimating, by processing circuitry, the crack on the physical structurewithin the electric machine based on a change of the measurement surfacemeasured by the measurement device, by estimating a displacement changeof a crack occurrence surface, which is inside the physical structurewithin the electric machine based on: a coefficient vector forming asparse solution acquired by solving a norm minimization problem bysetting, as parameters, the measurement surface change vector and a partof an estimation model, which is generated from a shape model obtainedby modeling a shape of the physical structure within the electricmachine; and another part of the estimation model.
 11. The estimationmethod according to claim 10, wherein the estimating a displacementchange includes: performing structure analysis based on a boundarycondition set in advance for the shape model generated based on themeasurement surface and the crack occurrence surface, to therebygenerate the estimation model including a plurality of measurementsurface estimated change vectors each estimating a change of themeasurement surface and a plurality of crack occurrence surfaceestimated change vectors each estimating the displacement change of thecrack occurrence surface; setting, as a measurement surface similarchange vector, a measurement surface estimated change vector having asimilarity with the measurement surface change vector higher than areference similarity set in advance, setting, as a crack occurrencesurface similar change vector, a crack occurrence surface estimatedchange vector corresponding to the measurement surface similar changevector and extracting the measurement surface similar change vectorserving as the part of the estimation model and the crack occurrencesurface similar change vector serving as the another part of theestimation model; extracting the coefficient vector by solving an L1norm minimization problem as the norm minimization problem based on themeasurement surface change vector and the measurement surface similarchange vector; and estimating a distribution of displacement changes ofthe crack occurrence surface based on the coefficient vector and thecrack occurrence surface similar change vector.
 12. The estimationmethod according to claim 11, further comprising: acquiring an amount ofexpansion of the crack on the crack occurrence surface based on a loadapplied to the physical structure within the electric machine and aphysical property value of the physical structure within the electricmachine; and determining a remaining usage period of the physicalstructure within the electric machine based on the amount of expansionof the crack and a size and a position of the crack in the physicalstructure within the electric machine.
 13. The estimation methodaccording to claim 12, further comprising outputting a notificationurging stop of usage of the physical structure within the electricmachine based on the amount of expansion of the crack and the size andthe position of the crack in the physical structure within the electricmachine.
 14. The estimation method according to claim 12, furthercomprising identifying a point having a maximum stress on the crackoccurrence surface, which occurs depending on the boundary condition, asan occurrence location of the crack.
 15. An estimation device toestimate a state of a crack on a physical structure within an electricmachine, the estimation device comprising: a measurement deviceconfigured to set an observation surface on a surface of the structureas a measurement surface to measure a change of the measurement surfaceas a measurement surface change vector; a model generator, implementedby processing circuitry, configured to perform structure analysis basedon a boundary condition set in advance for the shape model generatedbased on the measurement surface and crack occurrence surface, tothereby generate the estimation model including a plurality ofmeasurement surface estimated change vectors each estimating a change ofthe measurement surface and a plurality of crack occurrence surfaceestimated change vectors each estimating a displacement change of thecrack occurrence surface as the change of the crack occurrence surface;a similar vector extractor, implemented by the processing circuitry,configured to: set, as a measurement surface similar change vector, ameasurement surface estimated change vector having a similarity with themeasurement surface change vector higher than a reference similarity setin advance; set, as a crack occurrence surface similar change vector, acrack occurrence surface estimated change vector corresponding to themeasurement surface similar change vector; and extract the measurementsurface similar change vector serving as the part of the estimationmodel and the crack occurrence surface similar change vector serving asthe another part of the estimation model; a feature extractor,implemented by the processing circuitry, configured to extract thecoefficient vector by solving an L1 norm minimization problem as thenorm minimization problem based on the measurement surface change vectorand the measurement surface similar change vector; a crack analyzer,implemented by the processing circuitry, configured to estimate a changein distribution of displacement changes of the crack occurrence surfacebased on the coefficient vector and the crack occurrence surface similarchange vector; an estimator, configured to acquire position and size ofthe crack on the crack occurrence surface, based on distribution changeof the displacement change of the crack occurrence surface by estimatinga change of a crack occurrence surface, the load applied to thestructure, and the physical property value of the structure, from achange of a measurement surface measured by the measurement device; andan output module, configured to output the remaining usage periodgenerated based on the position and the size of the crack acquired bythe estimator or a notification urging stop of usage of the structuregenerated by the remaining usage period, into a speaker, a liquidcrystal display, or a light emitting device.